Does the neurodegeneration of Alzheimer’s disease spread between visual cortical regions B17 and B18 via the feedforward or feedback short cortico-cortical projections?

The laminar distribution of senile plaques (SP) and neurofibrillary tangles (NFT) was studied in areas B17 and B18 of the visual cortex in 18 cases of Alzheimer’s disease which varied in disease onset and duration. The objective was to test the hypothesis that SP and NFT could spread via either the feedforward or feedback short cortico-cortical projections. In area B17, the mean density of SP and NFT reached a maximum in lamina III and in laminae II and III respectively. In B18, mean SP density was maximal in laminae III and IV and NFT density in laminae II and III. No significant correlations were observed in any cortical lamina between the density of SP and patient age. However, the density of NFT in laminae III, IV and VI in B18 was negatively correlated with patient age. In addition, in B18, the density of SP in lamina II and lamina V was negatively correlated with disease duration and disease onset respectively. Although these results suggest that SP and NFT might spread between B17 and B18 via the feedforward short cortico-cortical projections, it is also possible that the longer cortico-cortical and cortico-subcortical connections may be involved.

Analyzing MSI rules for the USA extracted from a feedforward neural network

This paper introduces a mechanism for generating a series of rules that characterize the money price relationship for the USA, defined as the relationship between the rate of growth of the money supply and inflation. Monetary component data is used to train a selection of candidate feedforward neural networks. The selected network is mined for rules, expressed in human-readable and machine-executable form. The rule and network accuracy are compared, and expert commentary is made on the readability and reliability of the extracted rule set. The ultimate goal of this research is to produce rules that meaningfully and accurately describe inflation in terms of the monetary component dataset.

Neural control of a batch distillation

The purpose of the work reported here was to investigate the application of neural control to a common industrial process. The chosen problem was the control of a batch distillation. In the first phase towards deployment, a complex software simulation of the process was controlled. Initially, the plant was modelled with a neural emulator. The neural emulator was used to train a neural controller using the backpropagation through time algorithm. A high accuracy was achieved with the emulator after a large number of training epochs. The controller converged more rapidly, but its performance varied more widely over its operating range. However, the controlled system was relatively robust to changes in ambient conditions.

The time dimension of neural network models

This review attempts to provide an insightful perspective on the role of time within neural network models and the use of neural networks for problems involving time. The most commonly used neural network models are defined and explained giving mention to important technical issues but avoiding great detail. The relationship between recurrent and feedforward networks is emphasised, along with the distinctions in their practical and theoretical abilities. Some practical examples are discussed to illustrate the major issues concerning the application of neural networks to data with various types of temporal structure, and finally some highlights of current research on the more difficult types of problems are presented.

Neural networks for time-varying data

This paper reviews some basic issues and methods involved in using neural networks to respond in a desired fashion to a temporally-varying environment. Some popular network models and training methods are introduced. A speech recognition example is then used to illustrate the central difficulty of temporal data processing: learning to notice and remember relevant contextual information. Feedforward network methods are applicable to cases where this problem is not severe. The application of these methods are explained and applications are discussed in the areas of pure mathematics, chemical and physical systems, and economic systems. A more powerful but less practical algorithm for temporal problems, the moving targets algorithm, is sketched and discussed. For completeness, a few remarks are made on reinforcement learning.

The 'moving targets' training algorithm

A simple method for training the dynamical behavior of a neural network is derived. It is applicable to any training problem in discrete-time networks with arbitrary feedback. The method resembles back-propagation in that it is a least-squares, gradient-based optimization method, but the optimization is carried out in the hidden part of state space instead of weight space. A straightforward adaptation of this method to feedforward networks offers an alternative to training by conventional back-propagation. Computational results are presented for simple dynamical training problems, with varied success. The failures appear to arise when the method converges to a chaotic attractor. A patch-up for this problem is proposed. The patch-up involves a technique for implementing inequality constraints which may be of interest in its own right.

Computer control of chemical process plants with special reference to distillation

A sieve plate distillation column has been constructed and interfaced to a minicomputer with the necessary instrumentation for dynamic, estimation and control studies with special bearing on low-cost and noise-free instrumentation. A dynamic simulation of the column with a binary liquid system has been compiled using deterministic models that include fluid dynamics via Brambilla's equation for tray liquid holdup calculations. The simulation predictions have been tested experimentally under steady-state and transient conditions. The simulator's predictions of the tray temperatures have shown reasonably close agreement with the measured values under steady-state conditions and in the face of a step change in the feed rate. A method of extending linear filtering theory to highly nonlinear systems with very nonlinear measurement functional relationships has been proposed and tested by simulation on binary distillation. The simulation results have proved that the proposed methodology can overcome the typical instability problems associated with the Kalman filters. Three extended Kalman filters have been formulated and tested by simulation. The filters have been used to refine a much simplified model sequentially and to estimate parameters such as the unmeasured feed composition using information from the column simulation. It is first assumed that corrupted tray composition measurements are made available to the filter and then corrupted tray temperature measurements are accessed instead. The simulation results have demonstrated the powerful capability of the Kalman filters to overcome the typical hardware problems associated with the operation of on-line analyzers in relation to distillation dynamics and control by, in effect, replacirig them. A method of implementing estimator-aided feedforward (EAFF) control schemes has been proposed and tested by simulation on binary distillation. The results have shown that the EAFF scheme provides much better control and energy conservation than the conventional feedback temperature control in the face of a sustained step change in the feed rate or multiple changes in the feed rate, composition and temperature. Further extensions of this work are recommended as regards simulation, estimation and EAFF control.

Clustering and periodicity of neurofibrillary tangles in the upper and lower cortical laminae in Alzheimer's disease

In Alzheimer's disease (AD), neurofibrillary tangles (NFT) occur within neurons in both the upper and lower cortical laminae. Using a statistical method that estimates the size and spacing of NFT clusters along the cortex parallel to the pia mater, two hypotheses were tested: 1) that the cluster size and distribution of the NFT in gyri of the temporal lobe reflect degeneration of the feedforward (FF) and feedback (FB) cortico-cortical pathways, and 2) that there is a spatial relationship between the clusters of NFT in the upper and lower laminae. In 16 temporal lobe gyri from 10 cases of sporadic AD, NFT were present in both the upper and lower laminae in 11/16 (69%) gyri and in either the upper or lower laminae in 5/16 (31%) gyri. Clustering of the NFT was observed in all gyri. A significant peak-to-peak distance was observed in the upper laminae in 13/15 (87%) gyri and in the lower laminae in 8/ 12 (67%) gyri, suggesting a regularly repeating pattern of NFT clusters along the cortex. The regularly distributed clusters of NFT were between 500 and 800 μm in size, the estimated size of the cells of origin of the FF and FB cortico-cortical projections, in the upper laminae of 6/13 (46%) gyri and in the lower laminae of 2/8 (25%) gyri. Clusters of NFT in the upper laminae were spatially correlated (in phase) with those in the lower laminae in 5/16 (31%) gyri. The clustering patterns of the NFT are consistent with their formation in relation to the FF and FB cortico-cortical pathways. In most gyri, NFT clusters appeared to develop independently in the upper and lower laminae.

The spatial patterns of pathological brain lesions in 12 patients with corticobasal degeneration

Corticobasal degeneration (CBD) is a rare and progressive neurological disorder characterised by the presence of ballooned neurons (BN) and tau positive inclusions in neurons and glial cells. We studied the spatial patterns of the BN, tau positive neurons with inclusions (tau + neurons), and tau positive plaques in the neocortex and hippocampus in 12 cases of CBD. All lesions were aggregated into clusters and in many brain areas, the clusters were distributed in a regular pattern parallel to the tissue boundary. In the majority of cortical areas, the clusters of BN were larger in the lower compared with the upper laminae while the clusters of tau + neurons were larger in the upper laminae. Clusters of BN and tau + neurons were either negatively correlated or not significantly correlated in the upper and lower cortical laminae. Hence, BN and tau + lesions in CBD exhibit similar spatial patterns as lesions in Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and Pick's disease (PD). The location, sizes and distribution of the clusters in the neocortex suggest that the tau + lesions may be associated with the degeneration of the feedforward and the BN the feedback cortico-cortical and/or the efferent cortical pathways. © 2001 Elsevier Science Ireland Ltd. All rights reserved.

Continuous phase synchronised drives (for a rod-making machine)

Traditional high speed machinery actuators are powered and coordinated by mechanical linkages driven from a central drive, but these linkages may be replaced by independently synchronised electric drives. Problems associated with utilising such electric drives for this form of machinery were investigated. The research concentrated on a high speed rod-making machine, which required control of high inertias (0.01-0.5kgm2), at continuous high speed (2500 r/min), with low relative phase errors between two drives (0.0025 radians). Traditional minimum energy drive selection techniques for incremental motions were not applicable to continuous applications which require negligible energy dissipation. New selection techniques were developed. A brushless configuration constant enabled the comparison between seven different servo systems; the rate earth brushless drives had the best power rates which is a performance measure. Simulation was used to review control strategies, such that a microprocessor controller with a proportional velocity loop within a proportional position loop with velocity feedforward was designed. Local control schemes were investigated as means of reducing relative errors between drives: the slave of a master/slave scheme compensates for the master's errors: the matched scheme has drives with similar absolute errors so the relative error is minimised, and the feedforward scheme minimises error by adding compensation from previous knowledge. Simulation gave an approximate velocity loop bandwidth and position loop gain required to meet the specification. Theoretical limits for these parameters were defined in terms of digital sampling delays, quantisation, and system phase shifts. Performance degradation due to mechanical backlash was evaluated. Thus any drive could be checked to ensure that the performance specification could be realised. A two drive demonstrator was commissioned with 0.01kgm2 loads. By use of simulation the performance of one drive was improved by increasing the velocity loop bandwidth fourfold. With the master/slave scheme relative errors were within 0.0024 radians at a constant 2500 r/min for two 0.01 kgm^2 loads.

Laminar distribution of cortical Lewy bodies and neurofibrillary tangles in dementia with Lewy bodies

The laminar distribution of Lewy bodies (LB) and neurofibrillary tangles (NFT) was studied in twelve cases of dementia with Lewy bodies (DLB). LB density was maximal in the lower cortex in 59% of cortical areas, in the upper cortex in 31% of areas while densities were similar in the upper and lower cortex in 9% of areas. The distribution of LB was either unimodal with a lower cortical peak, or bimodal with density peaks in the upper and lower cortex. The density of NFT was maximal in the upper cortex in all tissues. The distributions of LB and NFT were similar in temporal and frontal cortex and in cases with and without Alzheimer’s disease (AD). The vertical densities of LB and NFT were not significantly correlated. LB formation may affect the feedback cortico-cortical pathway and the efferent cortical projections whereas NFT formation may affect the feedforward cortico-cortical pathway.

Neural networks for perpetual grouping

A number of researchers have investigated the application of neural networks to visual recognition, with much of the emphasis placed on exploiting the network's ability to generalise. However, despite the benefits of such an approach it is not at all obvious how networks can be developed which are capable of recognising objects subject to changes in rotation, translation and viewpoint. In this study, we suggest that a possible solution to this problem can be found by studying aspects of visual psychology and in particular, perceptual organisation. For example, it appears that grouping together lines based upon perceptually significant features can facilitate viewpoint independent recognition. The work presented here identifies simple grouping measures based on parallelism and connectivity and shows how it is possible to train multi-layer perceptrons (MLPs) to detect and determine the perceptual significance of any group presented. In this way, it is shown how MLPs which are trained via backpropagation to perform individual grouping tasks, can be brought together into a novel, large scale network capable of determining the perceptual significance of the whole input pattern. Finally the applicability of such significance values for recognition is investigated and results indicate that both the NILP and the Kohonen Feature Map can be trained to recognise simple shapes described in terms of perceptual significances. This study has also provided an opportunity to investigate aspects of the backpropagation algorithm, particularly the ability to generalise. In this study we report the results of various generalisation tests. In applying the backpropagation algorithm to certain problems, we found that there was a deficiency in performance with the standard learning algorithm. An improvement in performance could however, be obtained when suitable modifications were made to the algorithm. The modifications and consequent results are reported here.

Investigations of attentional processing in parietal and occipital human cortical regions with magnetoencephalography

Attention defines our mental ability to select and respond to stimuli, internal or external, on the basis of behavioural goals in the presence of competing, behaviourally irrelevant, stimuli. The frontal and parietal cortices are generally agreed to be involved with attentional processing, in what is termed the 'fronto-parietal' network. The left parietal cortex has been seen as the site for temporal attentional processing, whereas the right parietal cortex has been seen as the site for spatial attentional processing. There is much debate about when the modulation of the primary visual cortex occurs, whether it is modulated in the feedforward sweep of processing or modulated by feedback projections from extrastriate and higher cortical areas. MEG and psychophysical measurements were used to look at spatially selective covert attention. Dual-task and cue-based paradigms were used. It was found that the posterior parietal cortex (PPC), in particular the SPL and IPL, was the main site of activation during these experiments, and that the left parietal lobe was activated more strongly than the right parietal lobe throughout. The levels of activation in both parietal and occipital areas were modulated in accordance with attentional demands. It is likely that spatially selective covert attention is dominated by the left parietal lobe, and that this takes the form of the proposed sensory-perceptual lateralization within the parietal lobes. Another form of lateralization is proposed, termed the motor-processing lateralization, the side of dominance being determined by handedness, being reversed in left- relative to right-handers. In terms of the modulation of the primary visual cortex, it was found that it is unlikely that V1 is modulated initially; rather the modulation takes the form of feedback from higher extrastriate and parietal areas. This fits with the idea of preattentive visual processing, a commonly accepted idea which, in itself, prevents the concept of initial modulation of V1.

The application of H controller synthesis to high speed independent drive systems

This thesis describes work completed on the application of H controller synthesis to the design of controllers for single axis high speed independent drive design examples. H controller synthesis was used in a single controller format and in a self-tuning regulator, a type of adaptive controller. Three types of industrial design examples were attempted using H controller synthesis, both in simulation and on a Drives Test Facility at Aston University. The results were benchmarked against a Proportional, Integral and Derivative (PID) with velocity feedforward controller (VFF), the industrial standard for this application. An analysis of the differences between a H and PID with VFF controller was completed. A direct-form H controller was determined for a limited class of weighting function and plants which shows the relationship between the weighting function, nominal plant and the controller parameters. The direct-form controller was utilised in two ways. Firstly it allowed the production of simple guidelines for the industrial design of H controllers. Secondly it was used as the controller modifier in a self-tuning regulator (STR). The STR had a controller modification time (including nominal model parameter estimation) of 8ms. A Set-Point Gain Scheduling (SPGS) controller was developed and applied to an industrial design example. The applicability of each control strategy, PID with VFF, H, SPGS and STR, was investigated and a set of general guidelines for their use was determined. All controllers developed were implemented using standard industrial equipment.

Chromatic dispersion compensation using full-field maximum-likelihood sequence estimation

We investigate full-field detection-based maximum-likelihood sequence estimation (MLSE) for chromatic dispersion compensation in 10 Gbit/s OOK optical communication systems. Important design criteria are identified to optimize the system performance. It is confirmed that approximately 50% improvement in transmission reach can be achieved compared to conventional direct-detection MLSE at both 4 and 16 states. It is also shown that full-field MLSE is more robust to the noise and the associated noise amplifications in full-field reconstruction, and consequently exhibits better tolerance to nonoptimized system parameters than full-field feedforward equalizer. Experiments over 124 km spans of field-installed single-mode fiber without optical dispersion compensation using full-field MLSE verify the theoretically predicted performance benefits.